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README.md
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---
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license: mit
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library_name: transformers
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pipeline_tag: text-generation
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datasets:
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- yulan-team/YuLan-Mini-Datasets
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- HuggingFaceFW/fineweb-edu
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- bigcode/the-stack-v2
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- mlfoundations/dclm-baseline-1.0
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- math-ai/AutoMathText
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- gair-prox/open-web-math-pro
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- RUC-AIBOX/long_form_thought_data_5k
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- internlm/Lean-Workbook
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- internlm/Lean-Github
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- deepseek-ai/DeepSeek-Prover-V1
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- ScalableMath/Lean-STaR-base
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- ScalableMath/Lean-STaR-plus
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- ScalableMath/Lean-CoT-base
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- ScalableMath/Lean-CoT-plus
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- opencsg/chinese-fineweb-edu
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- liwu/MNBVC
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- vikp/textbook_quality_programming
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- HuggingFaceTB/smollm-corpus
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- OpenCoder-LLM/opc-annealing-corpus
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- OpenCoder-LLM/opc-sft-stage1
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- OpenCoder-LLM/opc-sft-stage2
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- XinyaoHu/AMPS_mathematica
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- deepmind/math_dataset
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- mrfakename/basic-math-10m
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- microsoft/orca-math-word-problems-200k
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- AI-MO/NuminaMath-CoT
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- HuggingFaceTB/cosmopedia
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- MU-NLPC/Calc-ape210k
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- manu/project_gutenberg
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- storytracer/LoC-PD-Books
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- allenai/dolma
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language:
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- en
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- zh
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tags:
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- code
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- math
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- llama-cpp
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- gguf-my-repo
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arxiv: 2412.17743
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base_model: yulan-team/YuLan-Mini
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model-index:
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- name: YuLan-Mini
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results:
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- task:
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type: text-generation
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dataset:
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name: HumanEval
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type: openai_humaneval
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metrics:
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- type: pass@1
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value: 0.64
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name: pass@1
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verified: false
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- task:
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type: text-generation
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dataset:
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name: MBPP
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type: mbpp
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metrics:
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- type: pass@1
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value: 0.659
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name: pass@1
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verified: false
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- task:
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type: text-generation
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dataset:
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name: MATH-500
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type: math-500
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metrics:
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- type: maj@1
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value: 0.378
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name: maj@1
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verified: false
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- task:
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type: text-generation
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dataset:
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name: GSM8K
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type: gsm8k
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metrics:
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- type: maj@1
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value: 0.684
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name: maj@1
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verified: false
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---
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# matrixportal/YuLan-Mini-Q8_0-GGUF
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This model was converted to GGUF format from [`yulan-team/YuLan-Mini`](https://huggingface.co/yulan-team/YuLan-Mini) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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Refer to the [original model card](https://huggingface.co/yulan-team/YuLan-Mini) for more details on the model.
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## Use with llama.cpp
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Install llama.cpp through brew (works on Mac and Linux)
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```bash
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brew install llama.cpp
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```
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Invoke the llama.cpp server or the CLI.
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### CLI:
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```bash
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llama-cli --hf-repo matrixportal/YuLan-Mini-Q8_0-GGUF --hf-file yulan-mini-q8_0.gguf -p "The meaning to life and the universe is"
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```
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### Server:
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```bash
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llama-server --hf-repo matrixportal/YuLan-Mini-Q8_0-GGUF --hf-file yulan-mini-q8_0.gguf -c 2048
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```
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Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
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Step 1: Clone llama.cpp from GitHub.
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```
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git clone https://github.com/ggerganov/llama.cpp
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```
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Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
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```
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cd llama.cpp && LLAMA_CURL=1 make
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```
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Step 3: Run inference through the main binary.
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```
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./llama-cli --hf-repo matrixportal/YuLan-Mini-Q8_0-GGUF --hf-file yulan-mini-q8_0.gguf -p "The meaning to life and the universe is"
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```
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or
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```
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./llama-server --hf-repo matrixportal/YuLan-Mini-Q8_0-GGUF --hf-file yulan-mini-q8_0.gguf -c 2048
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```
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